A Fast Facet-Based SAR Imaging Model and Target Detection Based on YOLOv5 with CBAM and Another Detection Head
Synthetic Aperture Radar (SAR) image target detection is of great significance in civil surveillance and military reconnaissance. However, there are few publicly released SAR image datasets of typical non-cooperative targets. Aiming to solve this problem, a fast facet-based SAR imaging model is prop...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/19/4039 |
| _version_ | 1797575993383190528 |
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| author | Qingkuan Wang Jing Sheng Chuangming Tong Zhaolong Wang Tao Song Mengdi Wang Tong Wang |
| author_facet | Qingkuan Wang Jing Sheng Chuangming Tong Zhaolong Wang Tao Song Mengdi Wang Tong Wang |
| author_sort | Qingkuan Wang |
| collection | DOAJ |
| description | Synthetic Aperture Radar (SAR) image target detection is of great significance in civil surveillance and military reconnaissance. However, there are few publicly released SAR image datasets of typical non-cooperative targets. Aiming to solve this problem, a fast facet-based SAR imaging model is proposed to simulate the SAR images of non-cooperative aircraft targets under different conditions. Combining the iterative physical optics and the Kirchhoff approximation, the scattering coefficient of each facet on the target and rough surface can be obtained. Then, the radar echo signal of an aircraft target above a rough surface environment can be generated, and the SAR images can be simulated under different conditions. Finally, through the simulation experiments, a dataset of typical non-cooperative targets can be established. Combining the YOLOv5 network with the convolutional block attention module (CBAM) and another detection head, a SAR image target detection model based on the established dataset is realized. Compared with other YOLO series detectors, the simulation results show a significant improvement in precision. Moreover, the automatic target recognition system presented in this paper can provide a reference for the detection and recognition of non-cooperative aircraft targets and has great practical application in situational awareness of battlefield conditions. |
| first_indexed | 2024-03-10T21:46:51Z |
| format | Article |
| id | doaj.art-972c5f613c97421b811c6fe093e7c20d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T21:46:51Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-972c5f613c97421b811c6fe093e7c20d2023-11-19T14:16:22ZengMDPI AGElectronics2079-92922023-09-011219403910.3390/electronics12194039A Fast Facet-Based SAR Imaging Model and Target Detection Based on YOLOv5 with CBAM and Another Detection HeadQingkuan Wang0Jing Sheng1Chuangming Tong2Zhaolong Wang3Tao Song4Mengdi Wang5Tong Wang6Air Defense and Antimissile School, Air Force Engineering University, Xi’an 710054, ChinaScience and Technology on Electromagnetic Scattering Laboratory, Beijing 100854, ChinaAir Defense and Antimissile School, Air Force Engineering University, Xi’an 710054, ChinaAir Defense and Antimissile School, Air Force Engineering University, Xi’an 710054, ChinaAir Defense and Antimissile School, Air Force Engineering University, Xi’an 710054, ChinaTroops 94789, People’s Liberation Army, Nanjing 210000, ChinaAir Defense and Antimissile School, Air Force Engineering University, Xi’an 710054, ChinaSynthetic Aperture Radar (SAR) image target detection is of great significance in civil surveillance and military reconnaissance. However, there are few publicly released SAR image datasets of typical non-cooperative targets. Aiming to solve this problem, a fast facet-based SAR imaging model is proposed to simulate the SAR images of non-cooperative aircraft targets under different conditions. Combining the iterative physical optics and the Kirchhoff approximation, the scattering coefficient of each facet on the target and rough surface can be obtained. Then, the radar echo signal of an aircraft target above a rough surface environment can be generated, and the SAR images can be simulated under different conditions. Finally, through the simulation experiments, a dataset of typical non-cooperative targets can be established. Combining the YOLOv5 network with the convolutional block attention module (CBAM) and another detection head, a SAR image target detection model based on the established dataset is realized. Compared with other YOLO series detectors, the simulation results show a significant improvement in precision. Moreover, the automatic target recognition system presented in this paper can provide a reference for the detection and recognition of non-cooperative aircraft targets and has great practical application in situational awareness of battlefield conditions.https://www.mdpi.com/2079-9292/12/19/4039electromagnetic scattering calculationSAR imageYOLOv5 networkconvolutional block attention module |
| spellingShingle | Qingkuan Wang Jing Sheng Chuangming Tong Zhaolong Wang Tao Song Mengdi Wang Tong Wang A Fast Facet-Based SAR Imaging Model and Target Detection Based on YOLOv5 with CBAM and Another Detection Head Electronics electromagnetic scattering calculation SAR image YOLOv5 network convolutional block attention module |
| title | A Fast Facet-Based SAR Imaging Model and Target Detection Based on YOLOv5 with CBAM and Another Detection Head |
| title_full | A Fast Facet-Based SAR Imaging Model and Target Detection Based on YOLOv5 with CBAM and Another Detection Head |
| title_fullStr | A Fast Facet-Based SAR Imaging Model and Target Detection Based on YOLOv5 with CBAM and Another Detection Head |
| title_full_unstemmed | A Fast Facet-Based SAR Imaging Model and Target Detection Based on YOLOv5 with CBAM and Another Detection Head |
| title_short | A Fast Facet-Based SAR Imaging Model and Target Detection Based on YOLOv5 with CBAM and Another Detection Head |
| title_sort | fast facet based sar imaging model and target detection based on yolov5 with cbam and another detection head |
| topic | electromagnetic scattering calculation SAR image YOLOv5 network convolutional block attention module |
| url | https://www.mdpi.com/2079-9292/12/19/4039 |
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